As with any Markov process, this sampling method requires a transition model, which in this case will generate new sample proposals from the current sample. This proposal density, \(q(x, y)\) , must satisfy a reversibility condition which states that the transition probability from a given state \(x\)  to a state \(y\)  must equal the probability of transitioning from \(y\)  to \(x\) . The MH algorithm is able to assure this condition [1]}.
